View source: R/ClassConstructors.R
make.ds.analysis  R Documentation 
This method creates an Analysis objects which describes a one or more models to fit to the distance data. The simulation will fit each of these models to the data generated in the simulation and select the model with the minimum criteria value.
make.ds.analysis( dfmodel = list(~1), key = "hn", truncation = numeric(0), cutpoints = numeric(0), er.var = "R2", control.opts = list(), group.strata = data.frame(), criteria = "AIC" )
dfmodel 
list of distance sampling model formula specifying the detection function
(see 
key 
key function to use; "hn" gives halfnormal (default) and "hr" gives hazardrate. 
truncation 
absolute truncation distance in simulation units matching the region units. 
cutpoints 
supply a vector of cutpoints if you wish the simulation to perform binned analyses. 
er.var 
encounter rate variance estimator to use when abundance estimates are
required. Defaults to "R2" for line transects and "P3" for point transects. See

control.opts 
A list of control options: method  optimisation method, 
group.strata 
Dataframe with two columns ("design.id" and "analysis.id"). The former gives the strata names as defined in the design (i.e. the region object) the second specifies how they should be grouped (into less strata) for the analyses. See details for more information. 
criteria 
character model selection criteria (AIC, AICc, BIC) 
It is possible to group strata at the analysis stage using the group.strata argument. For example, for design purposes it may have been sensible to divide strata into substrata. This can help make more convex shapes and therefore zigzag designs more efficient or perhaps it helped to keep transects angled parallel to density gradients across the study area. Despite these (purely design relevant) substrata we may still wish to calculate estimates of density / abundance etc. for each stratum. The table below gives an example of the data.frame which can be used to do this. Imagine a study region with an onshore strata and an offshore strata. The onshore strata has been divided in two at the design stage to keep transects perpendicular to the coast. We now want to analyse this as just two strata the onshore and offshore.
design.id  analysis.id 
   
onshoreN  onshore 
onshoreS  onshore 
offshore  offshore 
DS.Analysisclass
object
Laura Marshall
ds
make.simulation
# Model selection considering both a halfnormal and a hazardrate model # using AIC criteria and truncating 5% of the data ds.analyses < make.ds.analysis(dfmodel = ~1, key = c("hn", "hr"), truncation = 500, criteria = "AIC") # Model selection considering both a halfnormal with no covariates and with size # as a covariate using AIC criteria and truncating at 500 ds.analyses < make.ds.analysis(dfmodel = list(~1, ~size), key = "hn", truncation = 500, criteria = "AIC") # Model selection considering both a halfnormal with no covariates and with size # as a covariate and a hazard rate, using AIC criteria and truncating at 500 ds.analyses < make.ds.analysis(dfmodel = list(~1, ~size, ~1), key = c("hn", "hn", "hr"), truncation = 500, criteria = "AIC")
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